License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.CCC.2020.13
URN: urn:nbn:de:0030-drops-125650
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Coulson, Matthew ; Davies, Ewan ; Kolla, Alexandra ; Patel, Viresh ; Regts, Guus

Statistical Physics Approaches to Unique Games

LIPIcs-CCC-2020-13.pdf (0.6 MB)


We show how two techniques from statistical physics can be adapted to solve a variant of the notorious Unique Games problem, potentially opening new avenues towards the Unique Games Conjecture. The variant, which we call Count Unique Games, is a promise problem in which the "yes" case guarantees a certain number of highly satisfiable assignments to the Unique Games instance. In the standard Unique Games problem, the "yes" case only guarantees at least one such assignment. We exhibit efficient algorithms for Count Unique Games based on approximating a suitable partition function for the Unique Games instance via (i) a zero-free region and polynomial interpolation, and (ii) the cluster expansion. We also show that a modest improvement to the parameters for which we give results would be strong negative evidence for the truth of the Unique Games Conjecture.

BibTeX - Entry

  author =	{Matthew Coulson and Ewan Davies and Alexandra Kolla and Viresh Patel and Guus Regts},
  title =	{{Statistical Physics Approaches to Unique Games}},
  booktitle =	{35th Computational Complexity Conference (CCC 2020)},
  pages =	{13:1--13:27},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-156-6},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{169},
  editor =	{Shubhangi Saraf},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-125650},
  doi =		{10.4230/LIPIcs.CCC.2020.13},
  annote =	{Keywords: Unique Games Conjecture, approximation algorithm, Potts model, cluster expansion, polynomial interpolation}

Keywords: Unique Games Conjecture, approximation algorithm, Potts model, cluster expansion, polynomial interpolation
Collection: 35th Computational Complexity Conference (CCC 2020)
Issue Date: 2020
Date of publication: 17.07.2020

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